MiniMax-H3-FL2VA-Serve

About

What MiniMax-H3-FL2VA-Serve is

MiniMax-H3 FL2VA — MLX-Serve 8-bit 8-bit affine (group size 64) conversion of MiniMax-H3's FL2VA checkpoint for mlx-serve, running natively on Apple Silicon. Text-to-audio-video: the DiT denoises video and stereo audio jointly in one packed sequence. Self-contained: weights, both VAEs and the tokenizer in one directory. Upstream splits these across Comfy-Org/MiniMax-H3 (weights, no tokenizer) and MiniMaxAI/MiniMax-H3 (tokenizer). Quantized: the DiT and text-encoder matmul weights. Kept dense: gathered embedding tables, the checkpoint's fp32 islands (patch projections, output heads, time embedder) and both VAEs. Note that quantization here buys FOOTPRINT, not speed — the workload is compute-bound at roughly 192,000 FLOPs per weight byte. Modifications These are MODIFIED files. The transformer and text encoder are quantized to 8-bit; see MODIFICATIONS.md for the full list. The VAEs and tokenizer are unmodified copies. Turbo LoRA turbolora.safetensors is included. It is a 4-step distillation adapter trained by larryvrh and licensed Apache-2.0, copied byte for byte from that repo's minimaxh3turbo4stepemackpt850.safetensors and renamed so mlx-serve finds it inside the pack. Nothing about it was changed. It samples in 4 to 8 steps instead of the usual 16 to 30 (larryvrh notes 4 is already sharp on this checkpoint; earlier ones needed 6 to 8). Measured on an M4 Max at 960x544, 39 frames, one prompt: 188s of sampling at 16 steps without it against 95s at 4 steps with it, so roughly 2x on sampling and 1.8x on the whole request once staging and VAE decode are counted. Per step it is about 20% slower, because the adapter is summed at runtime rather than folded into the weights and it turns off mlx-serve's step-caching recipe. The win is the step count, not the step. Treat it as a preview. This is the EMA weighting of checkpoint 850, which larryvrh recommends as the cleanest of the round; the non-EMA file of the same step is sharper still but over-sharpened, and their notes describe plastic skin and over-sharp grain as the round's known failure modes. Their guidance if you hit either: nudge LoRA strength down to 0.8-0.95 for grain, up to 1.05-1.2 for blurry ghosting. Reques

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys minimax-h3-fl2va-serve for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (minimax-h3-fl2va-serve below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minimax-h3-fl2va-serve","prompt":"a drone shot over a forest"}'

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Commercial licence needed

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